Implementation details : For all real and fake videos, we use a state-of-the-art efficient face detector BlazeFace [1]} to detect faces of each frame and resize the facial images as inputs with a size of \(224\times 224\) . Random cropping and random horizontal flip are applied for data augmentation. We set size \(s=16\)  of blocks. Each image is divided into 196 blocks. We set the ratio \(q\sim U\left(0.4,0.6\right)\)  of intra-block shuffling. We apply the inter-block shuffling to all blocks of the image. In this paper, we adopt Xception [2]} as the backbone network of our BSL framework. We use Adam [3]} for optimization with a learning rate of \(1e^{-4}\)  and weight decay of \(1e^{-6}\) . We train our models on an Nvidia A10 GPU with batch size of 64.
